Reliable business intelligence starts long before data reaches a dashboard. As organizations adopt AI-driven analytics, data quality, lineage, and governance become essential foundations for trustworthy decisions.

This session explores practical architectural patterns for building AI-ready data platforms, including automated quality checks, metadata-driven pipelines, data reconciliation, lineage tracking, and risk-aware remediation.

We'll discuss how these capabilities support reliable Power BI reporting and how emerging agentic approaches can help identify and investigate data quality issues while maintaining human oversight.